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Deliang Wang; Yaqian Zheng; Jinjiang Li; Gaowei Chen – IEEE Transactions on Learning Technologies, 2025
Researchers have increasingly utilized artificial intelligence to automatically analyze classroom dialogue, aiming to provide timely feedback to teachers due to its educational significance. However, traditional machine learning and deep learning models face challenges, such as limited performance and lack of generalizability, across various…
Descriptors: Classroom Communication, Computational Linguistics, Cues, Generalization
Hershcovits, Haviv; Vilenchik, Dan; Gal, Kobi – IEEE Transactions on Learning Technologies, 2020
This paper studies students engagement in e-learning environments in which students work independently and solve problems without external supervision. We propose a new method to infer engagement patterns of users in such self-directed environments. We view engagement as a continuous process in time, measured along chosen axes that are derived…
Descriptors: Electronic Learning, Problem Solving, Independent Study, Factor Analysis
Santos, Marc Ericson C.; Chen, Angie; Taketomi, Takafumi; Yamamoto, Goshiro; Miyazaki, Jun; Kato, Hirokazu – IEEE Transactions on Learning Technologies, 2014
Augmented reality (AR) technology is mature for creating learning experiences for K-12 (pre-school, grade school, and high school) educational settings. We reviewed the applications intended to complement traditional curriculum materials for K-12. We found 87 research articles on augmented reality learning experiences (ARLEs) in the IEEE Xplore…
Descriptors: Simulated Environment, Educational Technology, Technology Uses in Education, Meta Analysis